As discussed in the above-referenced patents, it is known to store and query data that is modeled as a graph. Graphs can be networks of interrelated nodes, where each node is either a subject or an object and each edge is a named value, also called a predicate. Graphs can be represented by a collection of records, where members of records can point to other records. In this way, a triple is formed as [Record, MemberName, Record pointed to by value of MemberName]. An example of such a triple or fact might be “[a particular vehicle model] [has a model name] [‘A4’]”. A semantic store might store many facts of this form, as well as semantically related facts.
Graph-based data has been stored with special graph databases (e.g., Neo4j by Neo Technologies). As shown in the above-related patents, graph-based data can also be stored using a relational database as the underlying storage layer, with an application layer to provide access to the data using graph semantics. In either case, as information changes in a graph-based or semantic data store, information is often erased or otherwise destroyed; facts are sometimes physically deleted. Queries against this body of information are consequently invalidated or incomplete. In other words, results obtained from a query executed at one point in time might not be identically reproduced when the query is executed at a later point in time. Additionally, such systems or data stores have not supported querying of information in ways that involve inspecting the changes that have occurred in the data, nor have they supported maintaining and querying against information about lifetimes of facts even after facts have been “deleted”.
Techniques related to temporal information storage in semantic stores and temporal querying thereof are discussed below.